摘 要
随着数据量的急剧增长和应用场景的日益复杂，传统数据库查询优化方法面临诸多挑战，难以高效处理海量数据及复杂查询。为此，提出基于深度学习的数据库查询优化方法，旨在利用深度学习强大的特征提取与模式识别能力改善查询性能。该研究以提升查询效率、降低资源消耗为目的，通过构建神经网络模型对查询语句结构、数据库表结构以及历史查询日志进行综合分析，挖掘其中潜在规律并预测最优执行计划。创新性地引入强化学习机制，在模型训练过程中不断调整参数以适应不同查询场景，实现动态优化。实验结果表明，相较于传统基于规则或统计的方法，所提方法在多种测试集上平均响应时间减少约30%，准确率提高近20%，特别是在处理含有大量连接操作和子查询的复杂查询时优势明显。结论是基于深度学习的数据库查询优化方法能够有效应对现代数据库系统面临的挑战，为未来智能化数据库管理提供新思路，其出色的泛化能力和自适应性有望推动数据库技术向更高效、智能方向发展。
关键词：深度学习；数据库查询优化；强化学习
Abstract
With the rapid growth of data volumes and increasing complexity of application scenarios, traditional database query optimization methods face numerous challenges in efficiently processing massive datasets and complex queries. To address these issues, this study proposes a deep learning-based approach for database query optimization, leveraging the powerful feature extraction and pattern recognition capabilities of deep learning to enhance query performance. The ob jective is to improve query efficiency and reduce resource consumption by constructing neural network models that comprehensively analyze query statement structures, database table schemas, and historical query logs, thereby uncovering underlying patterns and predicting optimal execution plans. Innovatively, reinforcement learning mechanisms are introduced to dynamically adjust parameters during model training, adapting to diverse query scenarios and achieving dynamic optimization. Experimental results demonstrate that, compared with traditional rule-based or statistical methods, the proposed approach reduces average response time by approximately 30% and increases accuracy by nearly 20% across multiple test sets, particularly showing significant advantages in handling complex queries involving numerous join operations and subqueries. It is concluded that the deep learning-based database query optimization method can effectively tackle the challenges faced by modern database systems, providing new insights for future intelligent database management. Its outstanding generalization and adaptability hold promise for advancing database technology towards more efficient and intelligent directions.
Keywords：Deep Learning；Database Query Optimization；Reinforcement Learning
目 录
引 言 1
第一章 深度学习与数据库查询优化基础 2
1.1 数据库查询优化的基本概念 2
1.2 深度学习技术概述 2
1.3 深度学习应用于查询优化的必要性 3
第二章 查询代价估算的深度学习模型 4
2.1 传统查询代价估算方法 4
2.2 基于神经网络的代价估算模型 4
2.3 模型训练与评估指标 5
2.4 实验结果与分析 5
第三章 查询计划生成的深度学习方法 7
3.1 查询计划生成的传统算法 7
3.2 基于强化学习的计划生成 7
3.3 计划生成模型的优化策略 8
3.4 性能对比与案例研究 8
第四章 深度学习在索引优化中的应用 10
4.1 索引选择的传统方法 10
4.2 基于深度学习的索引推荐 10
4.3 动态索引调整机制 11
4.4 实际系统中的应用效果 11
结 论 13
参考文献 14
致 谢 15
随着数据量的急剧增长和应用场景的日益复杂，传统数据库查询优化方法面临诸多挑战，难以高效处理海量数据及复杂查询。为此，提出基于深度学习的数据库查询优化方法，旨在利用深度学习强大的特征提取与模式识别能力改善查询性能。该研究以提升查询效率、降低资源消耗为目的，通过构建神经网络模型对查询语句结构、数据库表结构以及历史查询日志进行综合分析，挖掘其中潜在规律并预测最优执行计划。创新性地引入强化学习机制，在模型训练过程中不断调整参数以适应不同查询场景，实现动态优化。实验结果表明，相较于传统基于规则或统计的方法，所提方法在多种测试集上平均响应时间减少约30%，准确率提高近20%，特别是在处理含有大量连接操作和子查询的复杂查询时优势明显。结论是基于深度学习的数据库查询优化方法能够有效应对现代数据库系统面临的挑战，为未来智能化数据库管理提供新思路，其出色的泛化能力和自适应性有望推动数据库技术向更高效、智能方向发展。
关键词：深度学习；数据库查询优化；强化学习
Abstract
With the rapid growth of data volumes and increasing complexity of application scenarios, traditional database query optimization methods face numerous challenges in efficiently processing massive datasets and complex queries. To address these issues, this study proposes a deep learning-based approach for database query optimization, leveraging the powerful feature extraction and pattern recognition capabilities of deep learning to enhance query performance. The ob jective is to improve query efficiency and reduce resource consumption by constructing neural network models that comprehensively analyze query statement structures, database table schemas, and historical query logs, thereby uncovering underlying patterns and predicting optimal execution plans. Innovatively, reinforcement learning mechanisms are introduced to dynamically adjust parameters during model training, adapting to diverse query scenarios and achieving dynamic optimization. Experimental results demonstrate that, compared with traditional rule-based or statistical methods, the proposed approach reduces average response time by approximately 30% and increases accuracy by nearly 20% across multiple test sets, particularly showing significant advantages in handling complex queries involving numerous join operations and subqueries. It is concluded that the deep learning-based database query optimization method can effectively tackle the challenges faced by modern database systems, providing new insights for future intelligent database management. Its outstanding generalization and adaptability hold promise for advancing database technology towards more efficient and intelligent directions.
Keywords：Deep Learning；Database Query Optimization；Reinforcement Learning
目 录
引 言 1
第一章 深度学习与数据库查询优化基础 2
1.1 数据库查询优化的基本概念 2
1.2 深度学习技术概述 2
1.3 深度学习应用于查询优化的必要性 3
第二章 查询代价估算的深度学习模型 4
2.1 传统查询代价估算方法 4
2.2 基于神经网络的代价估算模型 4
2.3 模型训练与评估指标 5
2.4 实验结果与分析 5
第三章 查询计划生成的深度学习方法 7
3.1 查询计划生成的传统算法 7
3.2 基于强化学习的计划生成 7
3.3 计划生成模型的优化策略 8
3.4 性能对比与案例研究 8
第四章 深度学习在索引优化中的应用 10
4.1 索引选择的传统方法 10
4.2 基于深度学习的索引推荐 10
4.3 动态索引调整机制 11
4.4 实际系统中的应用效果 11
结 论 13
参考文献 14
致 谢 15
